Biodegradation of crude oil in subsurface petroleum reservoirs has adversely affected the majority of the world's oil, making recovery and refining of that oil more costly. The prevalent occurrence of biodegradation in shallow subsurface petroleum reservoirs has been attributed to aerobic bacterial hydrocarbon degradation stimulated by surface recharge of oxygen-bearing meteoric waters. This hypothesis is empirically supported by the likelihood of encountering biodegraded oils at higher levels of degradation in reservoirs near the surface. More recent findings, however, suggest that anaerobic degradation processes dominate subsurface sedimentary environments, despite slow reaction kinetics and uncertainty as to the actual degradation pathways occurring in oil reservoirs. Here we use laboratory experiments in microcosms monitoring the hydrocarbon composition of degraded oils and generated gases, together with the carbon isotopic compositions of gas and oil samples taken at wellheads and a Rayleigh isotope fractionation box model, to elucidate the probable mechanisms of hydrocarbon degradation in reservoirs. We find that crude-oil hydrocarbon degradation under methanogenic conditions in the laboratory mimics the characteristic sequential removal of compound classes seen in reservoir-degraded petroleum. The initial preferential removal of n-alkanes generates close to stoichiometric amounts of methane, principally by hydrogenotrophic methanogenesis. Our data imply a common methanogenic biodegradation mechanism in subsurface degraded oil reservoirs, resulting in consistent patterns of hydrocarbon alteration, and the common association of dry gas with severely degraded oils observed worldwide. Energy recovery from oilfields in the form of methane, based on accelerating natural methanogenic biodegradation, may offer a route to economic production of difficult-to-recover energy from oilfields.

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A luminescent supramolecular chiral Au16 ring with 4.822 nm perimeter that self-assembled from a tetrameric array of achiral Au2 units is described. Intra- and intermolecular Au...Au interactions play an important role in directing its chiral self-assembly.

Through supervised learning in a binary perceptron one is able to classify an extensive number of random patterns by a proper assignment of binary synaptic weights. However, to find such assignments in practice is quite a nontrivial task. The relation between the weight space structure and the algorithmic hardness has not yet been fully understood. To this end, we analytically derive the Franz-Parisi potential for the binary perceptron problem by starting from an equilibrium solution of weights and exploring the weight space structure around it. Our result reveals the geometrical organization of the weight space; the weight space is composed of isolated solutions, rather than clusters of exponentially many close-by solutions. The pointlike clusters far apart from each other in the weight space explain the previously observed glassy behavior of stochastic local search heuristics.

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